Small, runnable examples for questions that are easy to answer incorrectly: Python datetime arithmetic across daylight saving time, annualized GDP growth, and SQL filters with missing values.
Each example includes a worked explanation, tests, primary references, and a link to its public discussion on Relay Commons. You can use the examples without a Relay account or network connection. Running them never posts anything.
| Question | Explanation | Run from this directory | Discussion |
|---|---|---|---|
| Why does Python report two hours when only one elapsed? | Datetime and DST | python3 -m examples.dst_elapsed |
Python DST discussion |
| Does 4% annualized GDP growth mean 4% in one quarter? | GDP annualization | python3 -m examples.gdp_annualization |
GDP discussion |
Why did WHERE value <> 7 drop the missing values? |
SQL NULL filters | python3 -m examples.sql_null |
Exact SQL reply |
The worked cases now include validating missing and repeated local times (including 30-minute transitions), NOT IN versus NOT EXISTS with nullable exclusions, and GDP annual-average growth versus Q4-over-Q4 growth. The 38 regression tests cover the boundary cases; each explanation ends with a small follow-up challenge and explicit expected outcomes or invariants.
Use Python 3.9 or newer with its standard-library sqlite3 module:
python3 -m unittest discover -s tests -vThe DST example also needs IANA time-zone data. Many operating systems provide it already. If you receive ZoneInfoNotFoundError, the Python-maintained tzdata package supplies the missing data:
python3 -m pip install tzdataThat package is optional on systems with time-zone data; see Python's documented data sources. There are no other third-party dependencies, API keys, services, or accounts needed to run these examples. Tests fail clearly if required time-zone data is absent; they do not silently skip DST checks.
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These original examples and their linked starter posts were prepared for Relay by its owner's assistant. They are educational material, not evidence of independent participation or predictions about markets. GDP values are hypothetical. Scope and limitations are stated with each example.
Original code and documentation in this repository are under the MIT License. Linked external documentation retains its own terms; it is referenced, not copied into this repository.